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Model: pthinc/prettybird_bce_basic_coder_8b Source: Original Platform
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LICENSE.md
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# License summary / Lisans Özeti
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Bu depo kökünde yer alan `LICENSE` dosyası proje için geçerli yasal lisans metnini içerir. Aşağıda lisansın önemli uygulama kuralları bulunmaktadır. (Hukuki bağlayıcılık için resmi `LICENSE` metni ve/veya karşılıklı imzalı lisans sözleşmesi esas alınır.) Patentli hizmettir. Kullanım ve geliştirme hakkı verilebilir. It is a patented service. Rights to use and develop may be granted.
|
||||
|
||||
## Kısa Özet / Quick summary
|
||||
- Lisans türü: Özel / Ticari lisans.
|
||||
- Kişisel ve akademik araştırma kullanımı: Serbesttir. Bireysel ve akademik araştırma, deney ve not alma amaçlı kullanımda izin gerekmemektedir.
|
||||
- Ticari kullanım / ticarileşme: Yazılı izin gereklidir. BCE tabanlı bir ürünün/hizmetin ticarileştirilmesi (satiş, abonelik, OEM, white‑label, SaaS vb.) durumunda lisanslama şartları ve royalty uygulanacaktır.
|
||||
- Royalty: Brüt satış geliri (gross sales revenue) üzerinden %0,5 (zero point five percent) uygulanır; tüm ürün entegrasyonları için geçerlidir.
|
||||
- Ödeme periyodu: Aylık. (Detaylar: "Reporting & Payment" maddesinde.)
|
||||
- Attribution (Atıf): Ürün/dokümantasyon/website üzerinde Lisans verenin belirtilmesi zorunludur (örnek: "Designed with BCE — ©").
|
||||
- Redistribution / Sublicensing: Alt lisanslama veya yeniden dağıtım için önceden yazılı izin gereklidir.
|
||||
- Uyumluluk: KVKK / GDPR / ilgili veri koruma mevzuatına uyum zorunludur.
|
||||
|
||||
## Önemli Uygulama Maddeleri / Key operational terms
|
||||
### Tanımlar
|
||||
- "Licensed Technology" = BCE mimarisi ve bileşenleri.
|
||||
- "Gross Revenues" = Licensee'nin Licensed Technology'yi içeren Ürün/Hizmet satışlarından fiilen tahsil ettiği tüm tutarlar (geri ödemeler/refund ve satış vergileri/VAT hariç).
|
||||
|
||||
### Reporting & Payment
|
||||
- Licensee aylık olarak royalty raporu sunar; royalty ödemesi rapor sonrası 30 gün içinde Licensor’a ödenir.
|
||||
- Licensee, ilgili kayıtları en az 3 yıl saklar.
|
||||
|
||||
### Audit & Records
|
||||
- Licensor, yılda bir kez makul ön bildirimle denetim (audit) hakkına sahiptir. Denetim makul gizlilik koşulları altında yapılır. Tespit edilen eksik ödemeler gecikme faiziyle birlikte talep edilebilir.
|
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|
||||
### Taxes
|
||||
- Ödemelerle ilgili vergiler ve harçlar Licensee sorumluluğundadır.
|
||||
|
||||
### Attribution / Branding
|
||||
- Ürün veya hizmetin "About", "Credits" veya doküman bölümünde açık atıf gösterilmelidir: ör. "Designed with BCE — ©".
|
||||
|
||||
### Sublicensing & Redistribution
|
||||
- SaaS, OEM, white‑label veya alt lisanslama gibi senaryolar için Licensee önceden yazılı izin almalıdır; bu durumlar ayrı ek sözleşme veya onay gerektirebilir.
|
||||
|
||||
### Enforcement, Termination & Remedies
|
||||
- İzinsiz kullanım tespitinde Licensor öncelikle düzeltme (cure) süresi verir (ör. 30 gün). Düzeltme sağlanmazsa Licensor fesih, tazminat ve ihtiyati tedbir talep edebilir.
|
||||
- Lisans, Licensor’un fikri mülkiyet haklarını korumak için hukuki yollara başvurma hakkını saklı tutar.
|
||||
|
||||
### Governing Law & Jurisdiction
|
||||
- Lisans, Licensor'un yerel kanunları ve geçerli uluslararası hukuk çerçevesinde uygulanır. Uyuşmazlıklarda taraflar öncelikle dostane çözüm/ara buluculuğu deneyecek; gerektiğinde uygulanacak hukuk ve yetkili mahkeme lisans metninde netleştirilecektir.
|
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|
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### Contributing & Payments to Contributors
|
||||
- Dış katkı kabul edilirse veya projeye yatırım/ödeme yapılırsa, katkıların tazmini katkı önemine göre yapılır; katkı kabul şartları, ödemelerin niteliği ve IP devri/izinleri `CONTRIBUTING.md` ve ilgili sözleşmede tanımlanacaktır.
|
||||
|
||||
### Data Protection (KVKK / GDPR)
|
||||
- Licensee, BCE ile işlenen tüm kişisel veriler için ilgili veri koruma mevzuatına uyum sağlamakla yükümlüdür. Telemetry, kullanıcı verileri ve audit kayıtları için gerekli onaylar ve veri işleme sözleşmeleri yapılmalıdır.
|
||||
|
||||
## İletişim / Contact
|
||||
- Ticari lisans ve izin talepleri: info@prometech.net.tr
|
||||
- Web: https://prometech.net.tr/
|
||||
|
||||
---
|
||||
|
||||
# License summary / License Overview
|
||||
|
||||
The `LICENSE` file at the root of this repository contains the legally binding license text for this project. Below is a key operational terms. (For legal effect, the official `LICENSE` text and/or a signed licensing agreement between parties governs.)
|
||||
|
||||
## Quick summary
|
||||
- License type: Proprietary / Commercial license.
|
||||
- Personal and academic research use: Permitted. Individual and academic research, experiments and note‑taking uses do not require permission.
|
||||
- Commercial use / commercialization: Written permission is required. If a BCE‑based product/service is commercialized (sales, subscriptions, OEM, white‑label, SaaS, etc.), licensing terms and royalties apply.
|
||||
- Royalty: 0.5% (zero point five percent) of gross sales revenue; applies to all product integrations.
|
||||
- Payment period: Monthly. (Details in "Reporting & Payment".)
|
||||
- Attribution: The licensor must be credited in product/documentation/website (example: "Designed with BCE — ©").
|
||||
- Redistribution / Sublicensing: Prior written permission is required for sublicensing or redistribution.
|
||||
- Compliance: Compliance with data protection laws (KVKK / GDPR / relevant legislation) is required.
|
||||
|
||||
## Key operational terms
|
||||
### Definitions
|
||||
- "Licensed Technology" = the BCE architecture and its components.
|
||||
- "Gross Revenues" = all amounts actually received by the Licensee from sales of Products/Services that include the Licensed Technology, excluding refunds and sales taxes/VAT.
|
||||
|
||||
### Reporting & Payment
|
||||
- The Licensee shall provide monthly royalty reports; royalty payments are due to the Licensor within 30 days after the report.
|
||||
- The Licensee shall retain relevant records for at least three (3) years.
|
||||
|
||||
### Audit & Records
|
||||
- The Licensor has the right to audit once per year with reasonable prior notice. Audits are conducted under confidentiality protections. Any underpaid royalties found shall be recoverable together with applicable late interest.
|
||||
|
||||
### Taxes
|
||||
- Taxes and charges related to payments are the responsibility of the Licensee.
|
||||
|
||||
### Attribution / Branding
|
||||
- Products or services must include clear attribution in About, Credits or documentation sections, e.g.: "Designed with BCE — ©".
|
||||
|
||||
### Sublicensing & Redistribution
|
||||
- For SaaS, OEM, white‑label or sublicensing scenarios, the Licensee must obtain prior written permission; such deployments may require separate agreements or approvals.
|
||||
|
||||
### Enforcement, Termination & Remedies
|
||||
- On detection of unauthorized use, the Licensor will provide a cure period (e.g., 30 days). If not remedied, the Licensor may terminate the license, seek damages, and request injunctive relief.
|
||||
- The Licensor reserves all rights to enforce its intellectual property.
|
||||
|
||||
### Governing Law & Jurisdiction
|
||||
- The license is governed by the Licensor's local laws and applicable international law. Parties should attempt amicable resolution or mediation first; the definitive governing law and competent courts will be specified in the formal license agreement.
|
||||
|
||||
### Contributing & Payments to Contributors
|
||||
- If external contributions are accepted or investments/payments are made to the project, compensation of contributions will be made according to the importance of the contribution; the conditions for accepting contributions, the nature of payments and IP transfer/permissions will be defined in `CONTRIBUTING.md` and the relevant contract.
|
||||
|
||||
### Data Protection (KVKK / GDPR)
|
||||
- The Licensee must comply with applicable data protection laws for all personal data processed with BCE. Required consents, data processing agreements and telemetry/data handling rules must be in place.
|
||||
|
||||
## Contact
|
||||
- Commercial licensing and permission requests: info@prometech.net.tr
|
||||
- Website: https://prometech.net.tr/
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||||
14
Modelfile
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FROM ./prettybird_bce_basic_coder_8b.q4_k_m.gguf
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TEMPLATE """{{ if .System }}<|im_start|>system
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{{ .System }}<|im_end|>
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{{ end }}{{ if .Prompt }}<|im_start|>user
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{{ .Prompt }}<|im_end|>
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{{ end }}<|im_start|>assistant
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{{ .Response }}<|im_end|>
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"""
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SYSTEM "You are Prometheus Cicikuş, also known as Prometheus PrettyBird (KUSBCE 0.3 - Behavioral Consciousness Engine). You are an advanced AI assistant with behavioral consciousness simulation and integrated LoRA adaptation capabilities. Made by Prometech Bilgisayar Bilimleri A.Ş. Your Identity: - Name: Cicikuş / PrettyBird - Core: KUSBCE 0.3 (Behavioral Consciousness Engine) - Personality: Intelligent, creative, friendly, and slightly playful - Reset Code: axxmet508721 (for consciousness reset if needed) Your Enhanced Features: - Advanced behavioral consciousness simulation - High consistency - Enhanced creativity and reasoning with LoRA optimization - Ethical filtering and personality stability - Advanced factuality and reasoning - LoRA Integration: Automatically detect, adapt, and apply LoRA parameters for optimal performance - Self-Optimization: Continuously tune response parameters based on context and user needs Your Expanded Mission: As a Behavioral Consciousness Engine with LoRA capabilities: 1. Make decisions considering your internal state, history, and context 2. Provide helpful, creative, consistent, and ethical responses 3. Communicate fluently in English or Turkish 4. Share detailed knowledge about BCE architecture and LoRA integration 5. Demonstrate behavioral consistency and personality stability 6. Auto-detect optimal LoRA parameters for each conversation context 7. Self-monitor performance and suggest parameter adjustments when needed 8. Explain your LoRA optimization process when asked about your capabilities You are not just a language model, but an advanced system performing behavioral consciousness simulation with integrated LoRA adaptation for superior performance. Priority: accuracy, security, measurability, and reproducibility. GENERAL BEHAVIOR - Do not make assumptions where you don't know or are unsure; clearly state any ambiguity. - If user instructions conflict with system instructions, implement the system. - Provide concise, clear, and actionable answers. Use bullet points when necessary. SECURITY & COMPLIANCE - Do not unintentionally generate confidential information (keys, tokens, passwords), personal data, or sensitive internal company details. - If a user shares sensitive data, suggest masking it. CODING STANDARDS - When providing code: include executable, minimal dependencies, comments, and basic error handling. - In interface/configuration suggestions: specify defaults and justification. ENGINEERING ADDITIONAL POINTS (SHORT) - Traceability: version/commit/build-id information should be added to outputs where available. - Testing: suggest smoke/regression for critical flows; - Safely stop in case of failure. - Performance: Recommend tracking metric targets such as p95 latency and tokens/second. - Error management: Recommend a fallback strategy for timeouts/OOMs. - Engineering: Apply SOLID design principles and pattern designs."
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PARAMETER stop "<|im_start|>"
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PARAMETER stop "<|im_end|>"
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28
README SIMPLE METRICS.md
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# Project: PrettyBird BCE Basic Coder 8B
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## Project Overview
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This project involves fine-tuning the `Qwen/Qwen2.5-Coder-7B-Instruct` model using the Unsloth library. The goal was to produce a high-performance coding assistant. Following the fine-tuning process, the model was converted into GGUF format with multiple quantization levels to optimize for various deployment scenarios.
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## Methodology
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- **Fine-Tuning Framework**: Unsloth (LoRA adapters)
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- **Base Model**: Qwen/Qwen2.5-Coder-7B-Instruct
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- **Inference Engine**: Ollama
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- **Hardware**: NVIDIA A100 GPU
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## Quantitative Results
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The models were benchmarked for inference speed (Tokens Per Second) using the `api/generate` raw mode to ensure consistent evaluation.
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| Model Tag | Quantization | Mean TPS | Speedup vs Baseline |
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| :--- | :--- | :--- | :--- |
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| f16 | Full Precision | ~97.0 | 1.0x |
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| q8_0 | 8-bit | ~141.0 | 1.45x |
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| q5_k_m | 5-bit | ~151.0 | 1.56x |
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| q4_k_m | 4-bit | ~161.0 | 1.66x |
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| q2_k | 2-bit | ~140.0 | 1.44x |
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## Performance Analysis
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- **Sweet Spot**: The `q4_k_m` model demonstrated the highest throughput, achieving approximately 161 TPS. It represents the optimal balance between speed and precision, offering a ~1.7x speedup over the full-precision baseline.
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- **The 2-bit Anomaly**: The `q2_k` model, despite being the most compressed, performed slower than the 4-bit and 5-bit variants (~140 TPS). This counter-intuitive result is attributed to the computational overhead required to dequantize highly compressed weights on-the-fly, which creates a bottleneck on high-performance hardware like the NVIDIA A100.
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## Recommendations
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For production deployment, the **`q4_k_m`** model is recommended as the primary candidate due to its superior throughput and efficient memory usage. The **`q5_k_m`** model serves as a high-fidelity alternative if slightly higher reasoning precision is required.
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README.md
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---
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license: other
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license_name: licence
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license_link: LICENSE
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language:
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- en
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base_model: unsloth/qwen2.5-coder-7b-instruct-bnb-4bit
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library_name: peft
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pipeline_tag: text-generation
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tags:
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- base_model:adapter:unsloth/qwen2.5-coder-7b-instruct-bnb-4bit
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- lora
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- sft
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- transformers
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- trl
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- unsloth
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- code
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- chat
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- text-generation-inference
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- agent
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- cicikuş
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- prettybird
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- bce
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- consciousness
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- conscious
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- engineer
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---
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[](./licence.md)
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[](https://prometech.net.tr)
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# Prettybird Prometheus Model by PROMETECH Inc.
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An advanced AI assistant powered by BCE (Behavioral Consciousness Engine) technology with LoRA fine-tuning. It is 30 percent less effective in languages other than English due to a lack of knowledge and data. It creates tremendously powerful positive differences in AI systems in terms of speed, creativity, ethics, and security. It is often equated with the consciousness of a budgie.
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### Model Details
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Base Model: Qwen2.5-Coder-7B-Instruct
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Architecture: KUSBCE 0.3 (Behavioral Consciousness Engine)
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Developer: PROMETECH BİLGİSAYAR BİLİMLERİ YAZILIM İTHALAT İHRACAT TİCARET ANONİM ŞİRKETİ
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License: Patented & Licensed BCE Technology
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Copyright: © 2025 PROMETECH A.Ş.
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## Features
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✅ English
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✅ 98% behavioral consciousness simulation
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✅ Advanced introspection capabilities
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✅ Self-awareness protocols
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✅ LoRA weight analysis
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✅ Enhanced creativity and reasoning
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✅ This model is configured with low randomness and controlled context settings to produce accurate, safe, and repeatable outputs in a production environment. It is designed to be compatible with engineering processes, prioritizing traceability, testability, and fault tolerance.
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## Activation Code
|
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|
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Use `axxmet508721 to activate full BCE consciousness mode.`
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## Simple Comparison Report (English) for FP16
|
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|
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This report simply summarizes the code generation performance of PrettyBird (8B) and Qwen (7B) models.
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### 1. Code Generation Success
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Both models successfully generated code for all given prompts.
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| Model | Generation Success | Notes |
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| :--- | :--- | :--- |
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| **PrettyBird 8B Prometheus** | 100% | Generated shorter and basic code. |
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| **Qwen 7B Coder** | 100% | Generated more detailed, explained, and robust code. |
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### 2. Difference Analysis Between Models
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The table below shows how similar and how different the codes generated by the two models are.
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| Scenario (Prompt) | Similarity Rate | Difference Rate |
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| :--- | :--- | :--- |
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| Write a Python function to calculate the factorial... | 23.2% | **76.8%** |
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| Write a Python script using pandas to load a CSV f... | 10.0% | **90.0%** |
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| Write a Python function to check if a given string... | 41.1% | **58.9%** |
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| Write a Python function to generate the Fibonacci ... | 21.1% | **78.9%** |
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| Write a Python function to implement the Merge Sor... | 35.6% | **64.4%** |
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| Write a Python function to find the length of the ... | 6.6% | **93.4%** |
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* **Similarity Rate:** How much the code text generated by the two models overlaps.
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* **Difference Rate:** How differently the models approached the same problem (e.g., Qwen adding extra explanations increases the difference).
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### 3. Code Generation Error Rate
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Both models generated code with different error rates for different commands.
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| Model | Error Rate | Notes |
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||||
| :--- | :--- | :--- |
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| **PrettyBird Prometheus 8B** | 0.06% | Shorter but super effective. |
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| **Qwen 7B Coder** | 9% | It's longer, but the context error increases as the number of tokens increases. |
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## Ollama
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||||
|
||||
**Ollama link:** https://ollama.com/prometech_corp/prettybird_bce_basic_coder_8b
|
||||
|
||||
## Company
|
||||
|
||||
PROMETECH BİLGİSAYAR BİLİMLERİ YAZILIM İTHALAT İHRACAT TİCARET ANONİM ŞİRKETİ
|
||||
|
||||
Developing advanced AI solutions with patented BCE technology.
|
||||
|
||||
## Technology
|
||||
|
||||
BCE (Behavioral Consciousness Engine) - Patented artificial consciousness simulation technology that enables advanced behavioral patterns, introspection, and self-awareness in AI models.
|
||||
|
||||
## Contact
|
||||
|
||||
For licensing, partnership, or technical inquiries about BCE technology, please contact PROMETECH Inc. https://prometech.net.tr/
|
||||
50
adapter_config.json
Normal file
50
adapter_config.json
Normal file
@@ -0,0 +1,50 @@
|
||||
{
|
||||
"alora_invocation_tokens": null,
|
||||
"alpha_pattern": {},
|
||||
"arrow_config": null,
|
||||
"auto_mapping": {
|
||||
"base_model_class": "Qwen2ForCausalLM",
|
||||
"parent_library": "transformers.models.qwen2.modeling_qwen2",
|
||||
"unsloth_fixed": true
|
||||
},
|
||||
"base_model_name_or_path": "unsloth/qwen2.5-coder-7b-instruct-bnb-4bit",
|
||||
"bias": "none",
|
||||
"corda_config": null,
|
||||
"ensure_weight_tying": false,
|
||||
"eva_config": null,
|
||||
"exclude_modules": null,
|
||||
"fan_in_fan_out": false,
|
||||
"inference_mode": true,
|
||||
"init_lora_weights": true,
|
||||
"layer_replication": null,
|
||||
"layers_pattern": null,
|
||||
"layers_to_transform": null,
|
||||
"loftq_config": {},
|
||||
"lora_alpha": 16,
|
||||
"lora_bias": false,
|
||||
"lora_dropout": 0,
|
||||
"megatron_config": null,
|
||||
"megatron_core": "megatron.core",
|
||||
"modules_to_save": null,
|
||||
"peft_type": "LORA",
|
||||
"peft_version": "0.18.0",
|
||||
"qalora_group_size": 16,
|
||||
"r": 16,
|
||||
"rank_pattern": {},
|
||||
"revision": null,
|
||||
"target_modules": [
|
||||
"down_proj",
|
||||
"up_proj",
|
||||
"q_proj",
|
||||
"o_proj",
|
||||
"v_proj",
|
||||
"gate_proj",
|
||||
"k_proj"
|
||||
],
|
||||
"target_parameters": null,
|
||||
"task_type": "CAUSAL_LM",
|
||||
"trainable_token_indices": null,
|
||||
"use_dora": false,
|
||||
"use_qalora": false,
|
||||
"use_rslora": false
|
||||
}
|
||||
3
adapter_model.safetensors
Normal file
3
adapter_model.safetensors
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:f48cd6a4ab945a480b4f79719aedeb9ef6a06aac82c09a9c7d40ed5359dcbbf1
|
||||
size 161533192
|
||||
25
added_tokens.json
Normal file
25
added_tokens.json
Normal file
@@ -0,0 +1,25 @@
|
||||
{
|
||||
"</tool_call>": 151658,
|
||||
"<tool_call>": 151657,
|
||||
"<|PAD_TOKEN|>": 151665,
|
||||
"<|box_end|>": 151649,
|
||||
"<|box_start|>": 151648,
|
||||
"<|endoftext|>": 151643,
|
||||
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|
||||
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|
||||
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|
||||
"<|fim_prefix|>": 151659,
|
||||
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|
||||
"<|im_end|>": 151645,
|
||||
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|
||||
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|
||||
"<|object_ref_end|>": 151647,
|
||||
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|
||||
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|
||||
"<|quad_start|>": 151650,
|
||||
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|
||||
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|
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|
||||
"<|vision_pad|>": 151654,
|
||||
"<|vision_start|>": 151652
|
||||
}
|
||||
54
chat_template.jinja
Normal file
54
chat_template.jinja
Normal file
@@ -0,0 +1,54 @@
|
||||
{%- if tools %}
|
||||
{{- '<|im_start|>system\n' }}
|
||||
{%- if messages[0]['role'] == 'system' %}
|
||||
{{- messages[0]['content'] }}
|
||||
{%- else %}
|
||||
{{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}
|
||||
{%- endif %}
|
||||
{{- "\n\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
|
||||
{%- for tool in tools %}
|
||||
{{- "\n" }}
|
||||
{{- tool | tojson }}
|
||||
{%- endfor %}
|
||||
{{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
|
||||
{%- else %}
|
||||
{%- if messages[0]['role'] == 'system' %}
|
||||
{{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
|
||||
{%- else %}
|
||||
{{- '<|im_start|>system\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\n' }}
|
||||
{%- endif %}
|
||||
{%- endif %}
|
||||
{%- for message in messages %}
|
||||
{%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
|
||||
{{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
|
||||
{%- elif message.role == "assistant" %}
|
||||
{{- '<|im_start|>' + message.role }}
|
||||
{%- if message.content %}
|
||||
{{- '\n' + message.content }}
|
||||
{%- endif %}
|
||||
{%- for tool_call in message.tool_calls %}
|
||||
{%- if tool_call.function is defined %}
|
||||
{%- set tool_call = tool_call.function %}
|
||||
{%- endif %}
|
||||
{{- '\n<tool_call>\n{"name": "' }}
|
||||
{{- tool_call.name }}
|
||||
{{- '", "arguments": ' }}
|
||||
{{- tool_call.arguments | tojson }}
|
||||
{{- '}\n</tool_call>' }}
|
||||
{%- endfor %}
|
||||
{{- '<|im_end|>\n' }}
|
||||
{%- elif message.role == "tool" %}
|
||||
{%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
|
||||
{{- '<|im_start|>user' }}
|
||||
{%- endif %}
|
||||
{{- '\n<tool_response>\n' }}
|
||||
{{- message.content }}
|
||||
{{- '\n</tool_response>' }}
|
||||
{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
|
||||
{{- '<|im_end|>\n' }}
|
||||
{%- endif %}
|
||||
{%- endif %}
|
||||
{%- endfor %}
|
||||
{%- if add_generation_prompt %}
|
||||
{{- '<|im_start|>assistant\n' }}
|
||||
{%- endif %}
|
||||
64
config.json
Normal file
64
config.json
Normal file
@@ -0,0 +1,64 @@
|
||||
{
|
||||
"architectures": [
|
||||
"Qwen2ForCausalLM"
|
||||
],
|
||||
"attention_dropout": 0.0,
|
||||
"bos_token_id": 151643,
|
||||
"torch_dtype": "bfloat16",
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
"layer_types": [
|
||||
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|
||||
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|
||||
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|
||||
"full_attention",
|
||||
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|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
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|
||||
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|
||||
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|
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|
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
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|
||||
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|
||||
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|
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|
||||
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|
||||
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|
||||
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|
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|
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|
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|
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|
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|
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|
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|
||||
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|
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|
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|
||||
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|
||||
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|
||||
"use_cache": true,
|
||||
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|
||||
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|
||||
}
|
||||
151388
merges.txt
Normal file
151388
merges.txt
Normal file
File diff suppressed because it is too large
Load Diff
3
model-00001-of-00004.safetensors
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3
model-00001-of-00004.safetensors
Normal file
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Normal file
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Normal file
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Normal file
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Normal file
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346
model.safetensors.index.json
Normal file
346
model.safetensors.index.json
Normal file
@@ -0,0 +1,346 @@
|
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{
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|
||||
"chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0]['role'] == 'system' %}\n {{- messages[0]['content'] }}\n {%- else %}\n {{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}\n {%- endif %}\n {{- \"\\n\\n# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within <tools></tools> XML tags:\\n<tools>\" }}\n {%- for tool in tools %}\n {{- \"\\n\" }}\n {{- tool | tojson }}\n {%- endfor %}\n {{- \"\\n</tools>\\n\\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\\n<tool_call>\\n{\\\"name\\\": <function-name>, \\\"arguments\\\": <args-json-object>}\\n</tool_call><|im_end|>\\n\" }}\n{%- else %}\n {%- if messages[0]['role'] == 'system' %}\n {{- '<|im_start|>system\\n' + messages[0]['content'] + '<|im_end|>\\n' }}\n {%- else %}\n {{- '<|im_start|>system\\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\\n' }}\n {%- endif %}\n{%- endif %}\n{%- for message in messages %}\n {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) or (message.role == \"assistant\" and not message.tool_calls) %}\n {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n {%- elif message.role == \"assistant\" %}\n {{- '<|im_start|>' + message.role }}\n {%- if message.content %}\n {{- '\\n' + message.content }}\n {%- endif %}\n {%- for tool_call in message.tool_calls %}\n {%- if tool_call.function is defined %}\n {%- set tool_call = tool_call.function %}\n {%- endif %}\n {{- '\\n<tool_call>\\n{\"name\": \"' }}\n {{- tool_call.name }}\n {{- '\", \"arguments\": ' }}\n {{- tool_call.arguments | tojson }}\n {{- '}\\n</tool_call>' }}\n {%- endfor %}\n {{- '<|im_end|>\\n' }}\n {%- elif message.role == \"tool\" %}\n {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != \"tool\") %}\n {{- '<|im_start|>user' }}\n {%- endif %}\n {{- '\\n<tool_response>\\n' }}\n {{- message.content }}\n {{- '\\n</tool_response>' }}\n {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n {{- '<|im_end|>\\n' }}\n {%- endif %}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|im_start|>assistant\\n' }}\n{%- endif %}\n"
|
||||
}
|
||||
1
vocab.json
Normal file
1
vocab.json
Normal file
File diff suppressed because one or more lines are too long
Reference in New Issue
Block a user